부산대학교 도서관

To access, purchase, authenticate, or subscribe to the full-text of this article, please visit this link: http://dx.doi.org/10.1016/S0140-6736(18)30595-6 Byline: Nafsiah Mboi, MD (a,b), Indra Murty Surbakti, PhD (d), Prof Indang Trihandini, PhD (e), Iqbal Elyazar, DPhil (f), Karen Houston Smith, EdM (c), Pungkas Bahjuri Ali, PhD (g), Soewarta Kosen, MD (c), Kristin Flemons, MA (h,i), Sarah E Ray, BS (i), Jackie Cao, MSc (i), Scott D Glenn, MSc (i), Molly K Miller-Petrie, MSc (i), Meghan D Mooney, BS (i), Jeffrey L Ried, PhD (i), Dina Nur Anggraini Ningrum, MPH (j,k), Prof Fachmi Idris, PhD (l,m), Kemal N Siregar, PhD (e), Pandu Harimurti, MD (n), Robert S Bernstein, MD (o,p), Prof Tikki Pangestu, PhD (q), Yuwono Sidharta, MD (r), Prof Mohsen Naghavi, PhD (i), Prof Christopher J L Murray, DPhil (i), Prof Simon I Hay, DSc [sihay@uw.edu] (i,s,*) Summary Background As Indonesia moves to provide health coverage for all citizens, understanding patterns of morbidity and mortality is important to allocate resources and address inequality. The Global Burden of Disease 2016 study (GBD 2016) estimates sources of early death and disability, which can inform policies to improve health care. Methods We used GBD 2016 results for cause-specific deaths, years of life lost, years lived with disability, disability-adjusted life-years (DALYs), life expectancy at birth, healthy life expectancy, and risk factors for 333 causes in Indonesia and in seven comparator countries. Estimates were produced by location, year, age, and sex using methods outlined in GBD 2016. Using the Socio-demographic Index, we generated expected values for each metric and compared these against observed results. Findings In Indonesia between 1990 and 2016, life expectancy increased by 8*0 years (95% uncertainty interval [UI] 7*3--8*8) to 71*7 years (71*0--72*3): the increase was 7*4 years (6*4--8*6) for males and 8*7 years (7*8--9*5) for females. Total DALYs due to communicable, maternal, neonatal, and nutritional causes decreased by 58*6% (95% UI 55*6--61*6), from 43*8 million (95% UI 41*4--46*5) to 18*1 million (16*8--19*6), whereas total DALYs from non-communicable diseases rose. DALYs due to injuries decreased, both in crude rates and in age-standardised rates. The three leading causes of DALYs in 2016 were ischaemic heart disease, cerebrovascular disease, and diabetes. Dietary risks were a leading contributor to the DALY burden, accounting for 13*6% (11*8--15*4) of DALYs in 2016. Interpretation Over the past 27 years, health across many indicators has improved in Indonesia. Improvements are partly offset by rising deaths and a growing burden of non-communicable diseases. To maintain and increase health gains, further work is needed to identify successful interventions and improve health equity. Funding The Bill & Melinda Gates Foundation. Author Affiliation: (a) Centre for Strategic and International Studies, Jakarta, Indonesia (b) National Commission for Tobacco Control, Jakarta, Indonesia (c) Independent consultant, Jakarta, Indonesia (d) Central Bureau of Statistics, Jakarta, Indonesia (e) Faculty of Public Health, University of Indonesia, Depok, Indonesia (f) Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia (g) National Development Planning Agency (BAPPENAS), Jakarta, Indonesia (h) Department of Anthropology, McGill University, Montreal, QC, Canada (i) Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA (j) Department of Public Health, Universitas Negeri Semarang, Semarang City, Indonesia (k) Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei City, Taiwan (l) Sriwijaya University, Palembang, Indonesia (m) Social Security Administering Body for Health, Jakarta, Indonesia (n) World Bank, Jakarta, Indonesia (o) Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA (p) Department of Global Health, College of Public Health, University of South Florida, Tampa, FL, USA (q) Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore (r) Field Epidemiology Training Program Indonesia, Jakarta, Indonesia (s) Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK * Correspondence to: Prof Simon I Hay, Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98121, USA